
"""
With this script you can evaluate checkpoints or test models from two popular
landmark retrieval github repos.
The first is https://github.com/naver/deep-image-retrieval from Naver labs,
provides ResNet-50 and ResNet-101 trained with AP on Google Landmarks 18 clean.
$ python eval.py --off_the_shelf=naver --l2=none --backbone=resnet101conv5 --aggregation=gem --fc_output_dim=2048

The second is https://github.com/filipradenovic/cnnimageretrieval-pytorch from
Radenovic, provides ResNet-50 and ResNet-101 trained with a triplet loss
on Google Landmarks 18 and sfm120k.
$ python eval.py --off_the_shelf=radenovic_gldv1 --l2=after_pool --backbone=resnet101conv5 --aggregation=gem --fc_output_dim=2048
$ python eval.py --off_the_shelf=radenovic_sfm --l2=after_pool --backbone=resnet101conv5 --aggregation=gem --fc_output_dim=2048

Note that although the architectures are almost the same, Naver's
implementation does not use a l2 normalization before/after the GeM aggregation,
while Radenovic's uses it after (and we use it before, which shows better
results in VG)
"""

import sys 
sys.path.append("..") 
sys.path.append("./") 


import os
import sys
import torch
import parser_3d
import logging
import sklearn
from os.path import join
from datetime import datetime
from torch.utils.model_zoo import load_url
from google_drive_downloader import GoogleDriveDownloader as gdd

import util

import commons
import datasets.datasets_ws as datasets_ws
from model import network

from model.extractors.FeatureVectorExtractor import FeatureVectorExtractor

from model.common.Context import Context

import faiss
import torch
import logging
import numpy as np
from tqdm import tqdm
from torch.utils.data import DataLoader
from torch.utils.data.dataset import Subset
from model.common.common import timestr
from model.search.LocFeature2ImgIndex import LocFeature2ImgIndex
from model.search.LocalFeatureSet import LocalFeatureSet
from model.postprocess.process import process_featureset, process_image


######################################### SETUP #########################################
args = parser_3d.parse_arguments()
# args.resume = "checkpoints/retrievalSfM120k-vgg16-gem-b4dcdc6.pth"
args.dataset_name = "pitts30k"

start_time = datetime.now()
args.save_dir = join("log", args.save_dir, start_time.strftime('%Y-%m-%d_%H-%M-%S'))
commons.setup_logging(args.save_dir)
commons.make_deterministic(args.seed)
logging.info(f"Arguments: {args}")
logging.info(f"The outputs are being saved in {args.save_dir}")

######################################### MODEL #########################################
model = FeatureVectorExtractor(args)
model = model.to(args.device)


# Enable DataParallel after loading checkpoint, otherwise doing it before
# would append "module." in front of the keys of the state dict triggering errors
model = torch.nn.DataParallel(model)

if args.pca_dim is None:
    pca = None
else:
    full_features_dim = args.features_dim
    args.features_dim = args.pca_dim
    # pca = util.compute_pca(args, model, args.pca_dataset_folder, full_features_dim)

from datasets.dataset_sp import DatasetSP
######################################### DATASETS #########################################

if args.datasets_type == "sp":
    test_ds = DatasetSP(args, args.datasets_folder, args.dataset_name, "test")
else:
    test_ds = datasets_ws.BaseDataset(args, args.datasets_folder, args.dataset_name, "test")
logging.info(f"Test set: {test_ds}")

######################################### TEST on TEST SET #########################################
context = Context()
context.dataset = test_ds
context.fve_model = model
context.test_method = args.test_method
context.pca = pca

eval_ds = context.dataset
pca = context.pca
model = context.fve_model.eval()
test_method = context.test_method
    
with torch.no_grad():
    logging.debug("Extracting database features for evaluation/testing")
    # For database use "hard_resize", although it usually has no effect because database images have same resolution
    eval_ds.test_method = "hard_resize"
    database_subset_ds = Subset(eval_ds, list(range(eval_ds.database_num)))
    database_dataloader = DataLoader(dataset=database_subset_ds, num_workers=args.num_workers,
                                        batch_size=args.infer_batch_size, pin_memory=(args.device == "cuda"))
    

    # faiss_index = faiss.IndexFlatL2(args.features_dim)
    # ind_index = LocFeature2ImgIndex()
    
            
    feature_index = LocalFeatureSet(args)
    logging.debug("Read features set")
    res = feature_index.read()
    logging.debug("Read features set done! "+"SUCCESS" if res >= 0 else "FALSE!")
    if res < 0:
        for inputs, indices in tqdm(database_dataloader, ncols=100):
            features = model(inputs.to(args.device))
            
            descriptors = features["descriptors"]
            if(torch.is_tensor(descriptors)):
                descriptors = descriptors.cpu().numpy()
                features["descriptors"] = descriptors
            if pca is not None:
                descriptors = pca.transform(descriptors)
                features["descriptors"] = descriptors
                
            imgid = indices.numpy()[0]
            feature_index.add_features(imgid,features)
        
            # fcnt = len(features)
            # ind_index.addIndices(imgid,fcnt)
            # faiss_index.add(features)
        # faiss.write_index()
        feature_index.write()
    
    if len(args.fve_post_process) > 0:
        feature_index2 = process_featureset(args.fve_post_process,args,feature_index,context=context)
        feature_index = feature_index2
    
    
    logging.debug("Extracting queries features for evaluation/testing")
    queries_infer_batch_size = 1
    eval_ds.test_method = test_method
            
    queries_subset_ds = Subset(eval_ds, list(range(eval_ds.database_num, eval_ds.database_num+eval_ds.queries_num)))
    queries_dataloader = DataLoader(dataset=queries_subset_ds, num_workers=args.num_workers,
                                    batch_size=queries_infer_batch_size, pin_memory=(args.device == "cuda"))
    
    query_result_dict = dict()

    with torch.profiler.profile(
    schedule=torch.profiler.schedule(wait=1, warmup=1, active=3, repeat=2),
    on_trace_ready=torch.profiler.tensorboard_trace_handler("./log/profiler/" + timestr()),
    record_shapes=True,
    profile_memory=True,
    with_stack=True
    ) as profiler:
        for inputs, indices in tqdm(queries_dataloader, ncols=100):

            query_index = indices.numpy()[0]
            # if not query_index % 10 == 0:
            #     continue

            if test_method == "five_crops" or test_method == "nearest_crop" or test_method == 'maj_voting':
                inputs = torch.cat(tuple(inputs))  # shape = 5*bs x 3 x 480 x 480
            features = model(inputs.to(args.device))

            descriptors = features["descriptors"]
            if(torch.is_tensor(descriptors)):
                descriptors = descriptors.cpu().numpy()
                features["descriptors"] = descriptors
            if pca is not None:
                descriptors = pca.transform(descriptors)
                features["descriptors"] = descriptors
            
            queryid = indices.numpy()[0]
            img = {"id":queryid,"features":features}
            
            if len(args.fve_post_process_query) > 0:
                features = process_image(args.fve_post_process_query,args,img,context=context)
                descriptors = features["descriptors"]
            
            # distances, predictions = faiss_index.search(features, max(args.recall_values))
            # predictions_ids = ind_index.searchArr2d(predictions)
            # predictions_ids_top_k = ind_index.most_common(predictions_ids,max(args.recall_values))
            
            predictions_ids_top_k = feature_index.search_image(descriptors)
            
            query_result_dict[queryid] = predictions_ids_top_k
            profiler.step()
        

        recalls = np.zeros(len(args.recall_values))
        for queryid, pred in query_result_dict.items():
            for i, n in enumerate(args.recall_values):
                q_res = eval_ds.query_positives(queryid,pred[:n])
                if q_res.shape[0] > 0:
                    recalls[i:] += 1
                    break
        # recalls = recalls / eval_ds.queries_num * 100
        recalls_str = ", ".join([f"R@{val}: {rec:.1f}" for val, rec in zip(args.recall_values, recalls)])
        print(recalls_str)


        logging.info(f"Recalls on {test_ds}: {recalls_str}")

        logging.info(f"Finished in {str(datetime.now() - start_time)[:-7]}")




# recalls, recalls_str = test_3d.test(args, context)



